@2uskiwis asked an interesting question. He had an upcoming TOU tariff change and he wanted a gross overview of how his bill would change even without knowing the future tariff rates. The tariff change was mainly a “shift” where the hours assigned to different TOU periods were shifted. Turns out I’m in the same situation, where I’m being asked to move from an EV-A TOU rate to EV2-A, which has a different TOU structure.
Now I’ve already been programming all my cars to charge after midnight, so I didn’t have to make any changes, but I am interested to see what the change looks like with respect to distribution/delta in TOU periods. To do this, I have to supply my calculation with:
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4 TOU period tables in 2 files. I chose to put the weekday tables for the current plan and the new plan in one .xlsx file and a the 2 corresponding weekend tables in a second .xlsx. The tables are borrowed from the way the OpenEI database documents TOU period information, except I use informative names instead of numbers.
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My utilities holiday dates that are treated as weekends for purposes of TOU, in a .csv file
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My custom billing cycle in a .csv file. PGE has different cycles for different users.
With those things, I can run the calculation and output a .csv that I can further analyze.
The output shows me good news… The new plan greatly reduces my Peak TOU usage, shifting it to Partial-Peak and Off-Peak usage.
The relevant files and R script are below:
R Script
ComparePlans.R (5.1 KB)
TOU Tables
WeekTable.xlsx (12.9 KB)
WeekEndTable.xlsx (12.7 KB)
Billing Cycle Table
pgebillingcycle.csv (706 Bytes)
Holidays Table
pgeholidays.csv (1.8 KB)
Sense Hourly Exports for 2019 and 2020
Add your own 
. I think it’s originally sourced from a British definition for tariff, but most US utilities refer to their list of rates as “tariff books”.